Rule-enriched Decision Tree Classifier for Conditional Sentence Sentiment Analysis
Email tác giả liên hệ:
huyentrangtin@gmail.comDOI:
https://doi.org/10.54644/jte.2024.1530Từ khóa:
Sentiment analysis, Conditional sentence, ReDTC, Sentence-level sentiment analysis, Conditional sentiment analysisTóm tắt
Conditional sentences are often used when people have to choose with some requirements. Conditional sentences account for more than 8% of user opinions. Although accounting for a considerable amount, for sentiment analysis methods, the sentiment expressed in conditional sentences is still analyzed as typical narrative sentences. This causes the approaches to fail to achieve maximum performance. To solve this problem, although some studies have proposed separate approaches to sentiment extraction and analysis for conditional sentences, there are very few, and the performance still needs to improve. This study proposes a new classifier based on a decision tree classifier model enriched with rules (called Rule-enriched Decision Tree Classifier (ReDTC)) to extract and analyze sentiments expressed in conditional sentences. ReDTC has been experimented on a dataset collected from English teaching websites. The performance gain demonstrates that the proposed ReDTC method significantly improved the performance in sentiment extraction and analysis in conditional sentences.
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Tài liệu tham khảo
H. T. Phan, N. T. Nguyen and D. Hwang, "Aspect-level sentiment analysis: A survey of graph convolutional network methods," Information Fusion, vol. 91, pp. 149-172, 2023. DOI: https://doi.org/10.1016/j.inffus.2022.10.004
H. T. Phan, D. T. Pham and N. T. Nguyen, "FeDN2: Fuzzy-Enhanced Deep Neural Networks for Improvement of Sentence-Level Sentiment Analysis," Cybernetics and Systems, pp. 1--17, 2023. DOI: https://doi.org/10.1080/01969722.2023.2296252
R. Narayanan, B. Liu and A. Choudhary, "Sentiment analysis of conditional sentences," in Proceedings of the 2009 conference on empirical methods in natural language processing, 2009, pp. 180-189. DOI: https://doi.org/10.3115/1699510.1699534
H. T. Phan, N. T. Nguyen, V. C. Tran and D. Hwang, "A method for detecting and analyzing the sentiment of tweets containing conditional sentences," in Asian Conference on Intelligent Information and Database Systems, Springer, 2019, pp. 177-188. DOI: https://doi.org/10.1007/978-3-030-14799-0_15
C. Hutto and E. Gilbert, "Vader: A parsimonious rule-based model for sentiment analysis of social media text," in Proceedings of the international AAAI conference on web and social media, 2014, pp. 216-225. DOI: https://doi.org/10.1609/icwsm.v8i1.14550
L. Im Tan, W. San Phang, K. O. Chin and A. Patricia, "Rule-based sentiment analysis for financial news," in 2015 IEEE International Conference on Systems, Man, and Cybernetics, IEEE, 2015, pp. 1601-1606. DOI: https://doi.org/10.1109/SMC.2015.283
K. Gao, H. Xu and J. Wang, "A rule-based approach to emotion cause detection for Chinese micro-blogs," Expert Systems with Applications, vol. 42, no. 9, pp. 4517-4528, 2015. DOI: https://doi.org/10.1016/j.eswa.2015.01.064
P. V. Ngoc, C. V. T. Ngoc, T. V. T. Ngoc and D. N. Duy, "A C4. 5 algorithm for english emotional classification," volving Systems, vol. 10, no. 3, pp. 425--451, 2019. DOI: https://doi.org/10.1007/s12530-017-9180-1
K. Cepeda and R. Jaiswal, "Sentiment analysis on covid-19 vaccinations in Ireland using support vector machine," in 2022 33rd Irish Signals and Systems Conference (ISSC), IEEE, 2022, pp. 1-6. DOI: https://doi.org/10.1109/ISSC55427.2022.9826215
O. Kellert, M. U. Zaman, N. H. Matlis and C. G'omez-Rodr'iguez, "Experimenting with ud adaptation of an unsupervised rule-based approach for sentiment analysis of mexican tourist texts," arXiv preprint arXiv:2309.05312, 2023.
D. Vilares, C. G'omez-Rodr'iguez and M. A. Alonso, "Universal, unsupervised (rule-based), uncovered sentiment analysis," Knowledge-Based Systems, vol. 118, pp. 45-55, 2017. DOI: https://doi.org/10.1016/j.knosys.2016.11.014
D. Zeman, J. Nivre, M. Abrams and others, "Universal Dependencies 2.6, LINDAT/CLARIAH-CZ digital library at the Institute of Formal and Applied Linguistics (UFAL), Faculty of Mathematics and Physics, Charles University," 2020.
J. R. Quinlan, C4. 5: programs for machine learning, Elsevier, 2014.
K. Yoon, "Convolutional Neural Networks for Sentence Classification," in Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing, EMNLP, Doha, Qatar, ACL, 2014, pp. 1746-1751.
K. Nal, G. Edward and B. Phil, "A Convolutional Neural Network for Modelling Sentences," in Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, Baltimore, MD, USA, ACL, 2014, pp. 655-665.
Y. Yang, "Convolutional Neural Networks with Recurrent Neural Filters," in Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, Brussels, Belgium, ACL, 2018, pp. 912-917. DOI: https://doi.org/10.18653/v1/D18-1109
M. Munikar, S. Shakya and A. Shrestha, "Fine-grained sentiment classification using BERT," in 2019 Artificial Intelligence for Transforming Business and Society (AITB), IEEE, 2019, pp. 1-5. DOI: https://doi.org/10.1109/AITB48515.2019.8947435
V. C. Tran, D. Hwang and N. T. Nguyen, "Hashtag recommendation approach based on content and user characteristics," Cybernetics and Systems, vol. 49, no. 5-6, pp. 368-383, 2018. DOI: https://doi.org/10.1080/01969722.2017.1418724
T. H. Duong, N. T. Nguyen and G. S. Jo, "A hybrid method for integrating multiple ontologies," Cybernetics and Systems: An International Journal, vol. 40, no. 2, pp. 123-145, 2009. DOI: https://doi.org/10.1080/01969720802634055
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